Abstract
Artificial bee colony (ABC) algorithm is a popular optimization technique with strong search ability. Although ABC has the ability to handle complex optimization problems, it suffers from weak exploitation and slow convergence. In order to tackle this issue, a new ABC variant based on multiple search strategies and dimension selection (ABC-MSDS) is proposed in this paper. Firstly, multiple search strategies based on dual strategy pool are designed. Compared to other existing ABC with multiple search strategies, our approach constructs two strategy pools for employed and onlooker bees, respectively. Secondly, a new dimension selection method is used to replace the random dimension selection in the standard ABC. In the search process, each dimension is chosen one by one in terms of the quality of offspring. Finally, a modified scout bee phase is employed to accelerate the search. Experimental study is conducted on classical benchmark problems and CEC 2013 shifted and rotated problems. The performance of ABC-MSDS is compared with several recently published ABC variants. Computational results demonstrate the effectiveness of our approach.
Highlights
In the past decades, some intelligent optimization algorithms have been proposed to solve complex and difficult problems, such as genetic algorithm (GA) [1], differential evolution (DE) [2]–[5], particle swarm optimization (PSO) [6]–[8], ant colony optimization (ACO) [9], artificial bee colony (ABC) [10], [11], and firefly algorithm (FA) [12]–[14], and bat algorithm (BA) [15], [16]
To tackle the above issues, we propose a new improved Artificial bee colony (ABC) algorithm called ABC-MSDS, which employs three modifications
Compared to ABC and multi-strategy ensemble ABC (MEABC), our approach designs a novel encoding method based on the dual strategy pool, in which a solution has three parts, position vector, search strategy for employed bees, and search strategy for onlooker bees
Summary
Some intelligent optimization algorithms have been proposed to solve complex and difficult problems, such as genetic algorithm (GA) [1], differential evolution (DE) [2]–[5], particle swarm optimization (PSO) [6]–[8], ant colony optimization (ACO) [9], artificial bee colony (ABC) [10], [11], and firefly algorithm (FA) [12]–[14], and bat algorithm (BA) [15], [16].
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